A Study on the Present Situation and Countermeasures of Translation Practice for English Majors in China
Bibliographic record
Abstract
This paper mainly uses the methods of questionnaire and qualitative analysis. A questionnaire survey was conducted among the last and current English majors. Mainly to understand their translation practice in school, including their attitude towards translation practice, the teaching mode of translation course, the time spent in translation practice after class and the main fields involved, and whether the school has provided translation practice platform and its practicability. The survey of 240 English majors reflects the present situation of English Majors in translation practice: 1) Strong willingness to translate; 2) Lack of practice in class; 3) Lack of extracurricular practice; 4) Lack of translation practice platform. To solve the problems in the translation practice of English majors analyzed by the survey results, this paper puts forward some countermeasures: including establishing a translation practice platform within the University, encouraging translation volunteer activities and building a cooperative translation mechanism between the University and the enterprise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".